The rapid evolution of computer science technologies has brought revolutionary advancements in modern data analytics. Today, we are capable of analyzing not only the data but also the depths of the context behind it. One technique that allows us to dig into the semantics of textual data is Named Entity Recognition (NER).
This article will cover the basics of named entity recognition and how to derive insights from text documents using NER in the context of building a Knowledge Graph.
What is Named Entity Recognition?
It is a Natural Language Processing (NLP) model that identifies named entities from text files and classifies them into predefined categories. NLP is a data processing technique that enables machines to understand human language. Based on the foundations of NLP, NER is a popular information extraction technique for scanning text documents to fetch essential entities. It is also known by other terms, including entity extraction, entity identification, or entity chunking.
What is an Entity?
An entity refers to the subjects or objects in a sentence that gives meaning to the text. Entities are nouns or noun phrases of any one of the following categories:
- Places/Geographical locations
- Dates and Times
- Monetary values, and more.
Consider, for example, the following sentence:
“Albert Einstein was born in Germany on March 14, 1879”.
The entities in the above sentence are:
Person: Albert Einstein, Place: Germany, and Date: March 14, 1879
The aim of named entity recognition is to give machines a human-like perception of understanding a sentence. Like human minds, NER enables machines to recognize individual word fragments and assign them to their accurate category.
How Does Named Entity Recognition Work?
While identifying and categorizing entities is a single-step task for human minds, machines operate differently. Machines have to break down these into separate tasks to complete an entity recognition process:
- Entity detection from a text file
- Entity classification into predefined categories
And this is where NER comes in. NER enables machines first to identify entities in the text and then designate them to relevant classes.
In the background of a NER model implementation on machines, NLP and Machine Learning have essential roles to play. NLP is involved in observing the rules of language and the morphology of words to build intelligent systems that can identify the context behind the text. On the other hand, machine learning makes machines automatically learn and improve over time, so the NER system keeps evolving to handle more complex text.
The first step is creating entities that will act as a resource for the NER model to match the text. The next step is to feed the NER model relevant training data to make the model learn based on these predefined entity categories. By tagging words and phrases in the training data to their relevant entities, the NER model learns to identify entities and eventually becomes capable of extracting information from unfamiliar text files.
What Are The Different Approaches to Named Entity Recognition?
Based on the vocabulary resources, the different techniques involved in named entity recognition are divided into four main categories:
- Lexicon/Dictionary-based NER
The most straightforward approach to NER is based on using a lexicon or dictionary as a vocabulary reference. The dictionary includes a limited set of entities that can be identified in the given text using basic string-matching algorithms. A dictionary's finite vocabulary collection limits the dictionary-based approach's efficiency. This technique works well as long the dictionary is appropriately updated and maintained.
- Rule-based NER
A rule-based approach to NER uses a predefined set of hand-crafted semantic rules for recognizing entities. Information extraction using the rule-based technique involves two main categories of rules:
- Pattern-based rules: These are rules that enable entity identification using the structural pattern of the words in a given text file.
- Context-based rules: These are rules that enable entity identification using the context behind the words in a given text file.
The rule-based method of NER requires manual construction of semantic rules and is often limited to specific domains.
- Machine learning-based NER
The machine learning-based approach to NER involves using statistical models to recognize entities in a given text document. Machine learning models observe textual data to create feature-based representations of entities. These representations allow NER systems to detect existing entities even if they are slightly misspelled.
ML-based NER requires model training on annotated textual data. The trained model is then used for annotating new files. In this way, ML-based NER systems self-improve by automatically evolving their entity knowledge base.
- Deep Learning-based NER
Deep learning has innovated many machine learning models in recent years. One of these models includes NER systems. Deep learning-based NER is a resource-friendly and time-saving approach to named entity recognition by eliminating the need for creating representations. Deep learning-based NER involves automated learning of entity representations from raw data to discover complex relations in given textual data. This is a relatively modern approach to NER that has revolutionized information extraction using advanced AI.
What is Named Entity Recognition Used For?
Named entity recognition can be beneficial in detecting crucial information, particularly from large datasets. NER can be applied in many different scenarios to add more meaning to the text.
Following are some of the common examples of systems that leverage named entity recognition:
Electronic Healthcare Systems
Electronic storage and management of healthcare data have opened doors for significant advancements in disease diagnosis and drug discovery. However, the biggest challenge in the way is sorting through the enormous pile of healthcare data to find symptoms, diseases, gene data, and more.
The NER models can be used for building robust medical systems that can identify and classify symptom information from healthcare records to deliver a timely and accurate disease diagnosis.
Customer Support Systems
Customer support systems today use a ticketing system for streamlining the management of customer queries and requests. NER models can be used to make these customer support ticketing systems faster and more efficient. NER can automate customer service tasks by extracting relevant information from customer issues that can be used for routing tickets to the right team that can handle the problem. This results in improved customer satisfaction and query resolution rates.
Customer experience is at the foundation of many modern applications, particularly entertainment apps, e.g., Netflix. These applications use recommendation systems to give customers a personalized view of content that best suits their interests. NER models are the engine behind many recommendation systems which recognize important entities from a customer's search history to make relevant content suggestions.
Resume Sorting Systems
Selecting the right candidate is critical for any organization that relies on sorting through hundreds of similar resumes. NER models help recruitment teams extract important candidate data from their resumes, including their personal and professional details. Recruiters can then use this categorized candidate information for hiring the most suitable candidates.
Customer Feedback Analysis
Customer feedback systems like online reviews hold important information that can help organizations improve their products and services. NER systems can be effective in extracting meaningful insights from the feedback data. It can be helpful in highlighting problem areas in negative customer reviews, allowing organizations to take proper steps to improve their customer satisfaction rates.
NER For Knowledge Graphs
In today's data-driven digital world, technologies that can organize and manage large volumes of data are becoming increasingly important. One of these technologies that organizations are widely adopting is knowledge graphs. Knowledge graphs help organizations visualize their enormous data to reveal ground-breaking insights. Knowledge graphs rely on natural language processing (NLP) to infer facts from organizational data. Most knowledge graphs face the challenge of identifying named entities in large textual datasets. To resolve this challenge, knowledge graphs can leverage named entity recognition models.
NER With Wisecube’s NLP-based Knowledge Graph Engine
Just like knowledge graphs, NER finds its applications in almost all domains. By deriving important details from natural language text, NER systems can amplify the benefits of a knowledge graph by building intelligent systems that can identify and prioritize required information in minutes.
If you want to extract the most value out of your data in no time, try Wisecube's Knowledge Graph Engine, which is based on advanced NLP technologies for extracting important entities and relationships from your data.
Schedule a call with us today.